{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e2ca255-74fb-4c63-951c-fdaaeaf485eb",
   "metadata": {},
   "source": [
    "# House predictions with linear regression using a toy dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ab6f999-9dbf-493d-b001-4e2ef4d25676",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run Coding_linear_regression.ipynb \n",
    "# allows us to use the functions we wrote\n",
    "\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d58fc0-95f8-45a6-948a-710c0cd2a4ef",
   "metadata": {},
   "source": [
    "### Defining and plotting our dataset\n",
    "\n",
    "Let's see how various implementations of Linear Regression work on our toy dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1386750d-e2b0-476a-a3d0-5c73bc281027",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'features': array([1, 2, 3, 5, 6, 7]), 'labels': array([155, 197, 244, 356, 407, 448])}\n"
     ]
    },
    {
     "data": {
      "image/png": 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jAeC8qs2XAQdrPZFoXlt2DNKzuZ+RI5XbR4PDI/Rs7gdgzarJehwjYrZNeKVge5HtJxevJ9k+s+p9LYEg4DZgj+0PVbUvqVrtFcDYl+O2AmslnSXpAuAiYNt0Tiqay/regTIQxowcGWV970CDKopYuGoaEG+aLgNeB/RL2lm0vQN4taSVVLqG9gNvBLC9S9ImYDeVJ5euy5NHC8PB4ZEptUdE/dQtFGx/m/HvE3xpkm3eC7y3XjXF6WlpawuD4wTA0taWBlQTsbDVMvNaRF11d3XQsnjRcW0tixfR3dXRoIoiFq56dh9F1GTsZvL63gEODo+wtLWF7q6O3GSOaICEQpwW1qxqTwhEnAbSfRQREaWEQkRElBIKERFRSihEREQpoRAREaWEQkRElBIKERFRSihEREQpoRAREaWEQkRElBIKERFRSihEREQpoRAREaWEQkRElBIKERFRSihEREQpoRAREaWEQkRElBIKERFRSihEREQpoRAREaW6hYKk8yR9XdIeSbskXV+0P0XSVyT9sPh5dtU2PZL2ShqQ1FWv2iIiYnz1vFI4CrzN9r8AngdcJ2kFcCNwl+2LgLuKzxTL1gKXAFcAH5W0qI71RUTECeoWCrYP2b63eP8wsAdoB64CNharbQTWFO+vAu6wfdj2PmAvsLpe9UVExMnOnIuDSFoOrALuAZ5q+xBUgkPSucVq7cDdVZsdKNpiAlt2DLK+d4CDwyMsbW2hu6uDNavyjywipq/uoSDpicDngRts/0LShKuO0+Zx9rcOWAdw/vnnz1aZTWfLjkF6NvczcmQUgMHhEXo29wMkGCJi2ur69JGkxVQC4Xbbm4vmByUtKZYvAR4q2g8A51Vtvgw4eOI+bW+w3Wm7s62trX7Fn+bW9w6UgTBm5Mgo63sHGlRRRMwH9Xz6SMBtwB7bH6patBW4pnh/DfDFqva1ks6SdAFwEbCtXvU1u4PDI1Nqj4ioRT27jy4DXgf0S9pZtL0DuAnYJOla4CfAqwBs75K0CdhN5cml62yPnrTXAGBpawuD4wTA0taWBlQTEfNF3ULB9rcZ/z4BwIsm2Oa9wHvrVdN80t3Vcdw9BYCWxYvo7upoYFUR0ezm5OmjmH1jN5Pz9FFEzKaEQhNbs6o9IRARsypjH0VERCmhEBERpYRCRESUEgoREVFKKERERCmhEBERpYRCRESUEgoREVFKKERERCmhEBERpYRCRESUEgoREVFKKERERCmhEBERpYRCRESUEgoREVFKKERERCmhEBERpYRCRESUEgoREVE6s9EFNMKWHYOs7x3g4PAIS1tb6O7qYM2q9kaXFRHRcAsuFLbsGKRncz8jR0YBGBweoWdzP0CCISIWvAXXfbS+d6AMhDEjR0ZZ3zvQoIoiIk4fdQsFSZ+Q9JCk+6va3iVpUNLO4vXSqmU9kvZKGpDUVa+6Dg6PTKk9ImIhqeeVwqeAK8Zp/7DtlcXrSwCSVgBrgUuKbT4qaVE9ilra2jKl9oiIhaRuoWD7m8DPalz9KuAO24dt7wP2AqvrUVd3Vwcti4/Pm5bFi+ju6qjH4SIimkoj7im8WdJ9RffS2UVbO/BA1ToHiraTSFonqU9S39DQ0JQPvmZVO++/+lLaW1sQ0N7awvuvvjQ3mSMimPunjz4G/BHg4ufNwO8BGmddj7cD2xuADQCdnZ3jrnMqa1a1JwQiIsYxp1cKth+0PWr7GHALj3URHQDOq1p1GXBwLmuLiIg5DgVJS6o+vgIYezJpK7BW0lmSLgAuArbNZW0REVHH7iNJfwW8EDhH0gHgncALJa2k0jW0H3gjgO1dkjYBu4GjwHW2R8fZbURE1JHsaXXLnxY6Ozvd19fX6DIiIpqKpO22O8dbtuC+0RwRERNr6isFSUPAj2ewi3OAn85SOY00X84Dci6no/lyHpBzGfNrttvGW9DUoTBTkvomuoRqJvPlPCDncjqaL+cBOZdapPsoIiJKCYWIiCgt9FDY0OgCZsl8OQ/IuZyO5st5QM7llBb0PYWIiDjeQr9SiIiIKgmFiIgoLbhQGG9GuGYl6TxJX5e0R9IuSdc3uqbpkvQ4Sdskfa84l3c3uqaZkLRI0g5Jdza6lpmQtF9SfzFTYlMPHyCpVdLnJH2/+H/mNxtd03RI6qiavXKnpF9IumHW9r/Q7ilIegHwCPBp289sdD0zUQwwuMT2vZKeBGwH1tje3eDSpkySgCfYfkTSYuDbwPW2725wadMi6Q+BTuDJtl/e6HqmS9J+oNN203/hS9JG4Fu2b5X0K8DjbQ83uKwZKWaoHASea3smX+QtLbgrhSnOCHdas33I9r3F+4eBPUwwOdHpzhWPFB8XF6+m/ItF0jLgZcCtja4lKiQ9GXgBcBuA7X9q9kAovAj40WwFAizAUJivJC0HVgH3NLiUaSu6XHYCDwFfsd2s5/IR4O3AsQbXMRsM/K2k7ZLWNbqYGXgaMAR8sujWu1XSExpd1CxYC/zVbO4woTAPSHoi8HngBtu/aHQ901VMwLSSyiRLqyU1XfeepJcDD9ne3uhaZslltp8NvAS4ruh+bUZnAs8GPmZ7FfAocGNjS5qZogvsSuCvZ3O/CYUmV/S/fx643fbmRtczG4rL+m8AVzS2kmm5DLiy6Iu/A7hc0mcaW9L02T5Y/HwI+AKPzZbYbA4AB6quPj9HJSSa2UuAe20/OJs7TSg0seLm7G3AHtsfanQ9MyGpTVJr8b4FeDHw/YYWNQ22e2wvs72cyqX912y/tsFlTYukJxQPMFB0tfxrHpstsanY/r/AA5I6iqYXUZnUq5m9mlnuOoI6zrx2uhpvRjjbtzW2qmm7DHgd0F/0xQO8w/aXGlfStC0BNhZPU5wBbLLd1I9zzgNPBb5Q+duDM4HP2v6bxpY0I28Bbi+6Xf4BeEOD65k2SY8H/hXF7JWzuu+F9khqRERMLN1HERFRSihEREQpoRAREaWEQkRElBIKERFRSihE05BkSTdXff4vkt41S/v+lKRXzsa+TnGcVxUjdH79hPblkkaKUS93S/p08cXEiDmVUIhmchi4WtI5jS6kWvHdilpdC/xn278zzrIfFcN8XEplqI/fnYXyIqYkoRDN5CiVeWn/4MQFJ/6lL+mR4ucLJf2dpE2SfiDpJkmvKeZu6Jd0YdVuXizpW8V6Ly+2XyRpvaTvSrpP0hur9vt1SZ8F+sep59XF/u+X9IGi7X8Azwc+Lmn9RCdpexTYRjHiraQXFYO49RfzgZx1ivb9kt4n6TuS+iQ9W1KvpB9JelOxzhJJ3yyuTO6X9Fu1/2uI+SyhEM3mz4HXSPpnU9jmWcD1VP4Cfx1wse3VVIa2fkvVesuB36Yy7PXHJT2Oyl/2/2j7OcBzgN+XdEGx/mrgv9leUX0wSUuBDwCXAyuB50haY/s9QB/wGtvdExVbHPe5wN8U7z8F/Dvbl1L5ZvF/mqi9ajcP2P5N4FvFeq8Enge8p1j+74He4srkWcDOCf/pxYKSUIimUowC+2ngrVPY7LvF3BOHgR8Bf1u091MJgjGbbB+z/UMqwyA8g8p4P68vhhG5B/hV4KJi/W22941zvOcA37A9ZPsocDuVsfxP5cLiOP8P+Int+4AOYJ/tHxTrbCz2NVH7mK1V53iP7YdtDwG/LMaY+i7whuKezKXFfBwRCYVoSh+h8hd89Xj4Ryn+ey4GCvyVqmWHq94fq/p8jOPH/zpxzBcDAt5ie2XxusD2WKg8OkF9qvE8TjR2T+HpwPMkXTnJvk51jOpzPPH8zywmm3oBlVm7/lLS66dZc8wzCYVoOrZ/BmyiEgxj9gO/Uby/isrMbVP1KklnFPcZngYMAL1UumsWA0i6uIbJWe4BflvSOcVN6FcDf1drEbYPURnrv4fKSLHLJT29WPy6Yl8TtddE0q9RmffhFioj7Tb7MNIxSxIK0axuBqqfQrqFyi/ibVT64yf6K34yA1R+sX4ZeJPtX1K577AbuFfS/cBfcIrRhYtf6j3A14HvURnz/otTrGUL8HgqXVFvAP5aUj+Vv/Q/XtR2UvsU9v9CYKekHcC/Bf5kivXFPJVRUiMiopQrhYiIKCUUIiKilFCIiIhSQiEiIkoJhYiIKCUUIiKilFCIiIjS/weklizMPOvfMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_rooms = numpy.array([1,2,3,5,6,7])\n",
    "price_rooms = numpy.array([155, 197, 244, 356,407,448])\n",
    "\n",
    "dict_data = {\n",
    "    'features': num_rooms,\n",
    "    'labels'  : price_rooms,\n",
    "}\n",
    "\n",
    "print(dict_data)\n",
    "\n",
    "plot_scatter(dict_data['features'], dict_data['labels'], \"Number of Rooms\", \"Price Per Room\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "578cb697-390f-48a0-b5f5-f536497f8e6b",
   "metadata": {},
   "source": [
    "### Linear Regression: Using our tricks\n",
    "\n",
    "We'll call the implementation we wrote in the other notebook along with some of its functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a99b7c0-423a-47ed-b0ed-9afa2f8e863f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 1351.26it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|█▏                                                                          | 149/10000 [00:00<00:06, 1486.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price per room: 64.6988135039272\n",
      "Base price: 31.81518936637274\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 10000/10000 [00:07<00:00, 1316.82it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price per room: 51.37932639433435\n",
      "Base price: 96.98880650149441\n"
     ]
    }
   ],
   "source": [
    "# We set the random seed in order to always get the same results.\n",
    "numpy.random.seed(0)\n",
    "\n",
    "# This line is for the x-axis to appear in the figure\n",
    "plt.ylim(0,500)\n",
    "slope, bias = linear_regression(\n",
    "    dict_data['features'],\n",
    "    dict_data['labels'],\n",
    "    trick_function = absolute_trick,\n",
    "    learning_rate = 0.05,\n",
    "    error_metric = rmse,\n",
    "    epochs = 1000)\n",
    "print('Price per room:', slope)\n",
    "print('Base price:', bias)\n",
    "\n",
    "# This line is for the x-axis to appear in the figure\n",
    "plt.ylim(0,500)\n",
    "slope, bias = linear_regression(\n",
    "    dict_data['features'],\n",
    "    dict_data['labels'],\n",
    "    trick_function = square_trick,\n",
    "    learning_rate = 0.01,\n",
    "    error_metric = rmse,\n",
    "    epochs = 10000)\n",
    "print('Price per room:', slope)\n",
    "print('Base price:', bias)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c4e2888-8a8e-4753-8f0c-e1513f0a3bcd",
   "metadata": {},
   "source": [
    "### Linear Regression: Using Turi Create\n",
    "\n",
    "You can install turicreate on Linux and Mac. Works on Windows via WSL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2e773ea-c9b2-4afe-a61b-9ad6510d8afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import turicreate as tc\n",
    "\n",
    "data = tc.SFrame(dict_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6af65c1-d628-4185-a1ea-49494bba02f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8350bacd-5612-4fe0-82c2-c8f3ef5c4a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tc.linear_regression.create(data, target='labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "789129bc-7f10-442d-82a7-12907e16c971",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c8164ae-bba5-43b3-bf2d-ba3da35acce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_point = tc.SFrame({'features': [4]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d64d184-96a8-40c2-b885-521d51f4b813",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.predict(new_point)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff060c4-f144-4447-b1f8-e9e83f581a2a",
   "metadata": {},
   "source": [
    "### Linear Regression: Using statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8046268f-7a9b-42a6-8934-833c50ecab7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# statsmodels doesn't automatically add an intercept (constant bias) column,\n",
    "# so we have to add that ourselves.\n",
    "exog = sm.add_constant(dict_data['features']) # adds an intercept column\n",
    "model_linear_regression = sm.OLS(\n",
    "    endog = dict_data['labels'], \n",
    "    exog = exog)\n",
    "results_linear_regression = model_linear_regression.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4382babb-b7f7-40d9-8b0e-355aca74d2cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Peanut\\miniconda3\\lib\\site-packages\\statsmodels\\stats\\stattools.py:75: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  \"samples were given.\" % int(n), ValueWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.998</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1941.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 05 Jul 2021</td> <th>  Prob (F-statistic):</th> <td>1.59e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:18:46</td>     <th>  Log-Likelihood:    </th> <td> -18.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>     6</td>      <th>  AIC:               </th> <td>   40.20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     4</td>      <th>  BIC:               </th> <td>   39.78</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   99.5952</td> <td>    5.200</td> <td>   19.155</td> <td> 0.000</td> <td>   85.159</td> <td>  114.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>   50.3929</td> <td>    1.144</td> <td>   44.060</td> <td> 0.000</td> <td>   47.217</td> <td>   53.568</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>   nan</td> <th>  Durbin-Watson:     </th> <td>   2.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td>   nan</td> <th>  Jarque-Bera (JB):  </th> <td>   0.833</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.124</td> <th>  Prob(JB):          </th> <td>   0.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.191</td> <th>  Cond. No.          </th> <td>    9.93</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.998\n",
       "Model:                            OLS   Adj. R-squared:                  0.997\n",
       "Method:                 Least Squares   F-statistic:                     1941.\n",
       "Date:                Mon, 05 Jul 2021   Prob (F-statistic):           1.59e-06\n",
       "Time:                        17:18:46   Log-Likelihood:                -18.100\n",
       "No. Observations:                   6   AIC:                             40.20\n",
       "Df Residuals:                       4   BIC:                             39.78\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         99.5952      5.200     19.155      0.000      85.159     114.031\n",
       "x1            50.3929      1.144     44.060      0.000      47.217      53.568\n",
       "==============================================================================\n",
       "Omnibus:                          nan   Durbin-Watson:                   2.022\n",
       "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.833\n",
       "Skew:                          -0.124   Prob(JB):                        0.659\n",
       "Kurtosis:                       1.191   Cond. No.                         9.93\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_linear_regression.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b5b3a412-a1ec-4ebe-902c-512a1964ea6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99.5952381 , 50.39285714])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_linear_regression.params # base-price and price per room"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "097c6e4f-09f5-44d5-919e-543b0cd3008c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tQA/gD8BNGY1KRIK1a8NM6nvuCReu778/XLjefPO4I5NqJJVRTwOiuyuA4zIbjoj818cfh26l6dPh9NPh8cfDDGuRLEtl1NMLlL4U6pUZiUikulu6FDp2hJdeCjWZ3n4bzjgj7qikGkul62lAwv0tgFbAosyEI1KNbdgAzz4LnTrBypXh9s47Yaut4o5MqrlUup76JT42s1eAoRmLSKQ6mjgxdDN9+ikcc0wY8tqkSdxRiQCpjXoqqQGgGsUi6fDjj3DjjWHC3Ny5YSTThx8qSUhOSeUaxUrCNQqLbpcAt2c4LpHC5g6vvQY33QRLloRCft26wXbbxR2ZyEZS6XpSAUCRdJo9G9q3hyFD4KCD4K234NBD445KpExJE4WZbQm0BorbweOAN9x9TaYDEyk4q1eHeRAPPBAmzfXooVnVkhfKvEZhZn8CZgB/JtR3mg/8BRhpZrXM7L6sRChSCN5/P9Rm6toVzj03lN5o315JQvJCshbF48A17j4kcaOZnQhMA6ZnMjCRgrBwYbhY3a9fWHFu6FA44YS4oxKpkGSjnnYtmSQA3H0oof5Tq4xFJZLv1q6Fhx8OCwcNHAj33QeTJytJSF5Klig2MbP/KbnRzLYgVJJdVdWTm9kpZjbLzOaYWadSnjczezx6foqZHVTVc4pk3MiRoeT3rbeGORHTp4d1q/9no/9OInkhWaLoA/Qzs3rFG6L7rwEvV/XEZlYDeAJoQbhYfpGZlRw83oIwb6MB0AboVdXzimTMsmVw1VVw1FGwfDm8+SYMGAB77RV3ZCJVUmaicPf7gPeBEWa2zMyWAcOBIe7eNQ3nPhSY4+5zo1FUfYEzS7zmTKCPB2OAWma2axrOLZI+xaU3GjYME+Y6doQZM6BVKzCLOzqRKks6PNbdewI9zWyb6PHKNJ57d+CrhMcLgcNSeM3uwOKSBzOzNoRWB3XrauK4ZMnkyWGI6+jR8Oc/h9Ib++4bd1QiaZVSCQ93X5nmJAFhpvdGp6rEa8JG997u3szdm9WuXbvKwYkktXJlWBfi4IPhiy/ghRdg+HAlCSlIqVSPzZSFwB4Jj+uwcVXaVF4jkj3u8MYbYcjrokXQpk2YRLf99nFHJpIxlSkKmC5jgQZmVt/MNgcuBN4p8Zp3gEuj0U/NgRXuvlG3k0hWzJkDLVrA+efDTjuF7qann1aSkIJXbqIws5pm9jczeyZ63MDMTqvqid19HdAe+IAwA/w1d59uZm3NrG30skHAXGAO8AzQrqrnFamwX36BLl1Ct9KoUWFp0rFjoXnzuCMTyYpUup5eAMYDh0ePFwKv8/sFjSrF3QcRkkHitqcS7jtwXVXPI1JpgwfDddeF1sQFF8Ajj8Buu8UdlUhWpdL1tLe7P0iYjY27r6b0i8wihePrr0Ni+MtfwhDXwYOhb18lCamWUkkUa6Iqsg5gZnsDv2Y0KpG4rFsXupYaNQprVXfpAlOmwEknxR2ZSGxS6Xq6mzDxbg8zKwKOBC7PZFAisRg9OsyJmDwZTjkFevaEvfeOOyqR2KWycNEQM5sANCd0OXVw92UZj0wkW777Djp1CrOrd989DH89+2zNqhaJlJkoSinAVzwsta6Z1XX3CZkLSyQLNmyAl16C224LtZluuQXuvhu20aKOIomStSgeTvKcA8enORaR7Jk6NXQzjRwJRxwBvXrBfvvFHZVITiozUbj7cdkMRCQrfvopXKB+9FGoVQueew4uvxw2iXPuqUhuK/caRbT+RDvgKEJL4mPgKXf/JcOxiaSPO/TvDx06hFXnrr4auneHHXaIOzKRnJfKn1F9gKZAD6AnYe2IKq9HIZI1c+fCaafBOeeEchsjR8IzzyhJiKQoleGxDd19/4THH5rZ5EwFJJI2v/4KDz4I//d/sOmmYVb19deH+yKSslT+x0w0s+bRwkGY2WHAyMyGJVJFQ4eG0huzZ8N554UkUadO3FGJ5KVUup4OA0aZ2TwzmweMBo4xs6lmNiWj0YlU1OLFcNFFYSb1+vXw3nvw2mtKEiJVkEqL4pSMRyFSVevWhSGud94Zqr3efTfcfjtsuWXckYnkvVRmZs83s+0ICwhtmrBdE+4kN3z2GbRtCxMnwsknh9IbDRrEHZVIwUhleGxXQm2n//DbMqSacCfxW74c7rgDeveGXXaBV18N1yNUekMkrVLpejqfUGp8TaaDEUmJO/TpAx07hjpNHTqESXR/+EPckYkUpFQSxTSgFvBtZkMRScH06dCuHYwYEVaYGzwYDjgg7qhECloqieJ+whDZaSSsQ+HuZ2QsKpGSfv4Z7r03DHPdZpvQ3XTVVSq9IZIFqSSKl4AHgKnAhsyGI1KCe1hAqEMHWLAArrgCHngAateOOzKRaiOVRLHM3R/PeCQiJc2bF2ZSDxgA++4LH38MRx0Vd1Qi1U4qiWK8md0PvMPvu540PFYyY80aeOghuO++0LX00ENwww2w2WZxRyZSLaWSKA6MbpsnbNPwWMmMDz8MF6tnzgyrzD32GOyxR9xRiVRrqUy407oUknlLlsCtt0JREey1FwwcCC1bxh2ViJBaiwIzO5VQanyL4m3ufm+mgpJqZP16eOop6NwZVq+Gv/0tTKJT6Q2RnJHKzOyngJrAccCzwLnAZxmOS6qDceNC6Y3x4+GEE+CJJ6Bhw7ijEpESUhmEfoS7Xwosd/cuwOGEuk8ilfPDD6EE+KGHwtdfwyuvwJAhShIiOSqVRLE6ul1lZrsBa4H6mQtJCpY7/POfISE89VQY+jpzJlx4oeozieSwVK5RDDCzWsDfgQmEEU/PZDIoKUAzZoTRTB99FFoS770HBx0Ud1QikoJURj11je72M7MBwBbuviKzYUnBWLUqzId46CHYaqvQkrjmGpXeEMkjZf5vNbNDzGyXhMeXAq8BXc1s+2wEJ3nu3XehSRO4/364+GKYNQv++lclCZE8k+x/7NPAGgAzOxroDvQBVgC9Mx+a5K358+Gss+CMM0IrYvhwePFF2GmnuCMTkUpIlihquPv30f0LgN7u3s/d/wb8MfOhSd5ZsyYU7GvSJIxieuABmDQJjj467shEpAqSXaOoYWabuvs64ASgTYr7SXU0fHi4WP3559CqVSi9Ubdu3FGJSBok+8J/BRhuZssIQ2Q/BjCzPxK6nyotusbxKlAPmAec7+7LS3ndPGAlsB5Y5+7NqnJeyYBvvw0rzfXpA/XqhesSp50Wd1QikkZldj25ezfgFuBF4Ch394R9rq/ieTsBw9y9ATAselyW49z9ACWJHFNceqNhwzBhrnPnsPqckoRIwUnaheTuY0rZNjsN5z0TODa6/xLwEXB7Go4r2TBhAlx7LXz2GRx3HDz5JDRqFHdUIpIhcY1T3NndFwNEt2UNh3FgsJmNN7M2ZbwGADNrY2bjzGzc0qVL0xyuALBiRVgX4pBDwsimoiIYNkxJQqTAZeyitJkNBXYp5anOFTjMke6+yMx2AoaY2Ux3H1HaC929N9Gw3WbNmnlpr5FKcoe+feHmm+Gbb0Kdpq5doVatuCMTkSzIWIvC3U90931L+Xkb+MbMdgWIbr8t4xiLottvgf7AoZmKV8owaxacdFKYMFenDowdCz16FESSKCoK19832STcFhXFHZFIboqr6+kd4LLo/mXA2yVfYGZbmdk2xfeBk4FpWYuwuiteG2K//UI58CefhDFj4OCD444sLYqKoE2b0IPmHm7btFGyEClNXImiO3CSmX0BnBQ9xsx2M7NB0Wt2Bj4xs8mE9S8Guvv7sURb3QwaBE2bhhpNF1wQWhXXXgs1asQdWdp07hzKUCVatSpsF5Hfi2XinLt/R5jEV3L7IqBldH8usH+WQ6vevvoKOnSA/v2hceOwfvWxx8YdVUYsWFCx7SLVmaqzCaxdG6q7Nm4M778fivhNmlSwSQLKnjSuyeQiG1OiqO4++SSsC9GxY1iO9PPPoVMn2HzzuCPLqG7doGbN32+rWTNsF5HfU6KorpYuhSuvhD//GX78Ed5+O/zUqxd3ZFnRujX07g177hkW19tzz/C4deu4IxPJPSruV91s2ADPPQe33w4rV4bWw513hnLg1Uzr1koMIqlQoqhOJk0Ko5fGjIFjjglDXps0iTsqEclx6nqqDn78EW68McyBmDs3VHr98EMlCRFJiVoUhcwdXnsNbroJliyBtm3D1drttos7MhHJI0oUheqLL6B9exg8OLQk3n47FPMTEakgdT0Vml9+gbvvhn33DdcievSATz9VkhCRSlOiKCTvvx8SxL33wrnnhtIb7dtXuPSGiuWJSCIlikLw9ddw3nnQogVsumlYI6KoCHYprcp7ciqWJyIlKVHks3Xr4JFHwsJBAwaEIn6TJ8Pxx1f6kCqWJyIl6WJ2vho1KsyJmDIFTj01XIuoX7/Kh1WxPBEpSS2KfPPdd3D11XDkkfD99/Dmm/Duu2lJEqBieSKyMSWKfFFceqNhQ3jppVDEb8YMaNUqFCtKExXLE5GSlCjywZQpoXjf1VeH2dQTJ8KDD8LWW6f9VCqWJyIl6RpFLlu5Eu65B/7xjzCb+oUX4LLL0tqCKI2K5YlIIiWKXOQO/fqF+kxffx3Gp95/P2y/fdyRiUg1pK6nXDNnDrRsGeZF1K4No0fD008rSYhIbJQocsUvv4QZ1fvuCyNHwmOPwdix0Lx53JGJSDWnrqdcMGQIXHddKOR3wQVhEt1uu8UdlYgIoBZFvBYtConh5JPD48GDoW9fJQkRySlKFHFYty6MZGrUKJT/7tIlDIE96aS4IxMR2Yi6nrJtzJhQemPSJDjlFOjZE/beO+6oRETKpBZFtnz/Pfz1r3DEEbB0KbzxBgwapCQhIjlPiSLT3OHFF0PpjeeeC8uSzpgB55yT8YlzIiLpoK6nTJo2LXQzffJJaEn06gX77Rd3VCIiFaIWRSb89FMo2nfAAaH18Nxz8PHHShIikpfUokgnd+jfHzp0gIUL4aqroHt32HHHuCMTEak0tSjSZe5cOO20cO1h++3D7Opnn1WSEJG8p0RRVb/+GpYgbdoURowIs6rHjw/XJERECoC6nqpi2DBo1w5mz4Zzz4VHH4U6deKOSkQkrdSiqIzFi+Hii+HEE2H9enjvPXj9dSUJESlIsSQKMzvPzKab2QYza5bkdaeY2Swzm2NmnbIZY6nWr4cePULpjX794K67YOrUMMNaRKRAxdWimAacDYwo6wVmVgN4AmgBNAEuMrMm2QmvFJ99BoccAjfcAIcdFuZIdOkCW24ZW0giItkQS6Jw9xnuPquclx0KzHH3ue6+BugLnJn56EpYvjxMmmveHJYsgVdfhQ8+gAYNsh6KiEgccvkaxe7AVwmPF0bbSmVmbcxsnJmNW7p0adXP7g59+oTSG717h7kRM2fC+eer9IaIVCsZG/VkZkOBXUp5qrO7v53KIUrZ5mW92N17A70BmjVrVubrUjJ9ehjNNGJEaEkMHhxmWYuIVEMZSxTufmIVD7EQ2CPhcR1gURWPmdzPP0PXrvDww7DNNqElcdVVsEkuN7xERDIrl+dRjAUamFl94GvgQuDijJ3t7bfDheoFC+Dyy+HBB6F27YydTkQkX8Q1PLaVmS0EDgcGmtkH0fbdzGwQgLuvA9oDHwAzgNfcfXrag5k3D844A846C/7wh9Dd9MILShIiIpFYWhTu3h/oX8r2RUDLhMeDgEEZCWLNGnjooVB+Y5NN4O9/DxesN9ssI6cTEclXudz1lDkffhguVs+cCWefDY89BnvsUe5uIiLVUbW4SltUBPXqwS72Df23/l84/vhQzG/gwDDDWklCRKRMBZ8oioqgzTVOy/lPMpOGnPrza3Tf9E763jkNWrYs/wAiItVcwSeKzp1h1WrjGIYznoP5E1O5Y11XOt1bM+7QRETyQsFfo1iwINxewQusZkuK5/EVbxcRkeQKvkVRt264XU1NEid7F28XEZHkCj5RdOsGNUv0MtWsGbaLiEj5Cj5RtG4dKnHsuWeo5bfnnuFx69ZxRyYikh8K/hoFhKSgxCAiUjkF36IQEZGqUaIQEZGklChERCQpJQoREUlKiUJERJIy96qtGpqLzGwpML8Kh9gRWJamcNJJcaUuF2MCxVURuRgTFG5ce7p7qQvxFGSiqCozG+fuzeKOoyTFlbpcjAkUV0XkYkxQPeNS15OIiCSlRCEiIkkpUZSud9wBlEFxpS4XYwLFVRG5GBNUw7h0jUJERJJSi0JERJJSohARkaSqbaIws/PMbLqZbTCzMoeUmdkpZjbLzOaYWaeE7dub2RAz+yK63S5NcZV7XDNraGaTEn5+NLMbo+fuMbOvE56r8sLgqf5bzWyemU2NzjuuovtnIi4z28PMPjSzGdHvu0PCc2l7r8r6nCQ8b2b2ePT8FDM7KNV9qyKFuFpH8Uwxs1Fmtn/Cc6X+PrMU17FmtiLhd3NXqvtmMKaOCfFMM7P1ZrZ99Fwm36vnzexbM5tWxvOZ/2y5e7X8ARoDDYGPgGZlvKYG8B9gL2BzYDLQJHruQaBTdL8T8ECa4qrQcaMYlxAmywDcA9ya5vcqpZiAecCOVf03pTMuYFfgoOj+NsDshN9hWt6rZJ+ThNe0BN4jLLPYHPg01X0zHNcRwHbR/RbFcSX7fWYprmOBAZXZN1MxlXj96cC/M/1eRcc+GjgImFbG8xn/bFXbFoW7z3D3WeW87FBgjrvPdfc1QF/gzOi5M4GXovsvAWelKbSKHvcE4D/uXpWZ6OmOKd37V/q47r7Y3SdE91cCM4Dd03T+Ysk+J4mx9vFgDFDLzHZNcd+MxeXuo9x9efRwDFAnTeeuUlwZ2jedx70IeCUN5y2Xu48Avk/ykox/tqptokjR7sBXCY8X8tuXzM7uvhjClxGwU5rOWdHjXsjGH9j2URP0+TR186QakwODzWy8mbWpxP6ZigsAM6sHHAh8mrA5He9Vss9Jea9JZd/KquixryL8ZVqsrN9ntuI63Mwmm9l7Zta0gvtmKibMrCZwCtAvYXOm3qtUZPyzVdAr3JnZUGCXUp7q7O5vp3KIUrZVeTxxsrgqeJzNgTOAOxI29wK6EuLsCjwMXJmlmI5090VmthMwxMxmRn8NVVoa36utCf+xb3T3H6PNlXqvSjt8KdtKfk7Kek1GPmPlnHPjF5odR0gURyVsTvvvswJxTSB0p/4UXTt6C2iQ4r6ZiqnY6cBId0/8Kz9T71UqMv7ZKuhE4e4nVvEQC4E9Eh7XARZF978xs13dfXHUzPs2HXGZWUWO2wKY4O7fJBz7v/fN7BlgQLZicvdF0e23Ztaf0PQdQczvlZltRkgSRe7+ZsKxK/VelSLZ56S812yewr6VlUpcmNl+wLNAC3f/rnh7kt9nxuNKSOa4+yAze9LMdkxl30zFlGCjVnwG36tUZPyzpa6n5MYCDcysfvTX+4XAO9Fz7wCXRfcvA1JpoaSiIsfdqJ80+sIs1goodaREumMys63MbJvi+8DJCeeO7b0yMwOeA2a4+yMlnkvXe5Xsc5IY66XRCJXmwIqouyyVfSur3GObWV3gTeASd5+dsD3Z7zMbce0S/e4ws0MJ31XfpbJvpmKKYtkWOIaEz1qG36tUZP6zlYmr9PnwQ/hiWAj8CnwDfBBt3w0YlPC6loSRMv8hdFkVb98BGAZ8Ed1un6a4Sj1uKXHVJPzH2bbE/i8DU4Ep0Ydi12zERBhZMTn6mZ4r7xWhK8Wj92NS9NMy3e9VaZ8ToC3QNrpvwBPR81NJGGlX1mcsTe9ReXE9CyxPeG/Glff7zFJc7aPzTiZcZD8i0+9XeTFFjy8H+pbYL9Pv1SvAYmAt4Tvrqmx/tlTCQ0REklLXk4iIJKVEISIiSSlRiIhIUkoUIiKSlBKFiIgkpUQhOcvMdrDfqnUusd8qvf5gZp9nOZazzKxJwuN7zazCEzrNrJ6VXQW0qZn928xmm9l/zKyLmaX9/2iyf4uZfWRJqilL9aREITnL3b9z9wPc/QDgKeDR6P4BwIZ0n8/MklUqOAv475eru9/l7kPTeO4tCXM5urv7PsCfCLN7OyTdsXLOIoP/Fik8ShSSr2qY2TMW1pgYHH3RYmZ7m9n7UXG2j82sUbR9TzMbZqEA4LBoRjJm9qKZPWJmHwIPlLa/mR1BqKn196hFs3e037nRMQ6xsJbDZDP7zMy2iVoOH5vZhOjniHL+PRcT6gcNBnD3VYRJZx2jc9xjZrcWv9jCegj1ovtvRfFOt4SCdGb2k5l1i+IaY2Y7l/dvSWRmJ5vZ6Cj+1y3Uy8LMupvZ59F7+VDFf3WSb5QoJF81AJ5w96bAD8A50fbewPXufjBwK/BktL0noRTzfkAR8HjCsfYBTnT3W0rb391HEf7a7xi1cP5TvGNUGuFVoIO77w+cCKwm1J06yd0PAi4ocb7SNAXGJ26IzrOlmdUqZ98ro3ibATeY2Q7R9q2AMVFcI4Brkv1bElmoq3Rn9L4cBIwDbrawUE8roGn0Xt5XTmxSAAq6KKAUtC/dfVJ0fzxQL/qL9wjg9ahMEMD/RLeHA2dH918mLHpU7HV3X1/O/mVpCCx297HwWzG7qOZPTzM7AFhPSEbJGKVX9iytAmhJN5hZq+j+HoQk+h2wht8KHY4HTkrhWMWaE7qnRkbvxebAaOBH4BfgWTMbSOULKUoeUaKQfPVrwv31wJaEFvIP0XWM8iR+Kf8c3VZk/2JlfcHfRKghtn903F/KOc50wkpmvx3YbC9gmbv/YGbr+H0PwBbRa44ltGIOd/dVZvZR8XPAWv+tRs96Kvb/3YAh7n7RRk+EIn0nEIrMtQeOr8BxJQ+p60kKRvTX/Jdmdh78dy3h4jWgRxG+2ABaA59UcP+VhKVUS5oJ7GZmh0T7bBNdFN+W0NLYAFxCWJYymSLgqITRR1sSuqvujp6fR1gOEwtrItePtm8LLI+SRCNCS6A8Zf1bEo0BjjSzP0bnrGlm+0Strm3dfRBwI2FggRQ4JQopNK2Bq8ysuJJn8dKPNwBXmNkUwhd3WaOJytq/L9DRzCaa2d7FL/awxOQFQI9onyGEv+ifBC4zszGEbqefScLdVxMuMnc2s9nAMsLF7aLoJf2A7c1sEnAtoSIowPvAptG/qyvhC748pf5bSsSzlFAp9ZXo2GOARoQEMyDaNpzQcpICp+qxIjnIzM4CHgGO88yuhy5SLiUKERFJSl1PIiKSlBKFiIgkpUQhIiJJKVGIiEhSShQiIpKUEoWIiCT1/wFuJkIv6rS5owAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(\n",
    "    results_linear_regression.fittedvalues, \n",
    "    results_linear_regression.resid,\n",
    "    x_label = \"Fitted Values\",\n",
    "    y_label = \"Residual Values\")\n",
    "plt.show()\n",
    "\n",
    "# Not sure why it plots twice\n",
    "sm.qqplot(results_linear_regression.resid_pearson, line = \"q\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b408888c-99be-4674-ae1c-1f1db3fba0bb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
